“Prelert is dedicated to making it easier for users to analyze their
data and drive real, actionable value from it”

Earlier this year, Prelert released its
Engine API enabling developers and power users to leverage its
advanced analytics algorithms in their operations monitoring and
security architectures. By offering an Elasticsearch Connector, the
company further strengthens its commitment to democratizing the use of
machine learning technology, providing tools that make it even easier to
identify threats and opportunities hidden within massive data sets.

Written in Python, the Prelert Elasticsearch Connector source is
available on GitHub. This enables developers to apply Prelert’s
advanced, machine learning-based analytics to fit the big data needs
within their unique environment.

“Prelert is dedicated to making it easier for users to analyze their
data and drive real, actionable value from it,” said Mark Jaffe, CEO,
Prelert. “The amounts of data that companies and organizations have
these days are simply massive – too massive for humans to process and
analyze. The release of our Elasticsearch Connector is the latest step
toward making the analysis of large data sets possible, repeatable and
valuable without a team of data scientists.”

Prelert’s Anomaly Detective processes huge volumes of streaming data,
automatically learns normal behavior patterns represented by the data
and identifies and cross-correlates any anomalies. It routinely
processes millions of data points in real-time and identifies
performance, security and operational anomalies so they can be acted on
before they impact business.

The Elasticsearch Connector is the first connector to be officially
released by Prelert. Additional connectors to several of the most
popular technologies used with big data will be released throughout the
coming months.

About PrelertPrelert is the anomaly detection company. Its
automated behavioral analytics make it easy for users and developers to
uncover real-time insights into the operational opportunities and risks
hidden in massive data sets. By using unsupervised machine learning
technology, Prelert enables non-data scientists to go beyond the limits
of search to quickly derive value from their organization’s data. To
learn more, please visit www.prelert.com
or follow @Prelert.